Overview

Dataset statistics

Number of variables17
Number of observations368
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.8 KiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical2

Alerts

start_date has a high cardinality: 368 distinct values High cardinality
end_date has a high cardinality: 368 distinct values High cardinality
TOTAL SPECIMENS is highly correlated with TOTAL A and 5 other fieldsHigh correlation
TOTAL A is highly correlated with TOTAL SPECIMENS and 5 other fieldsHigh correlation
TOTAL B is highly correlated with TOTAL A and 3 other fieldsHigh correlation
PERCENT POSITIVE is highly correlated with TOTAL SPECIMENS and 4 other fieldsHigh correlation
PERCENT A is highly correlated with TOTAL SPECIMENS and 4 other fieldsHigh correlation
PERCENT B is highly correlated with TOTAL A and 3 other fieldsHigh correlation
ILITOTAL is highly correlated with TOTAL SPECIMENS and 3 other fieldsHigh correlation
NUM. OF PROVIDERS is highly correlated with TOTAL SPECIMENS and 2 other fieldsHigh correlation
TOTAL PATIENTS is highly correlated with TOTAL SPECIMENS and 2 other fieldsHigh correlation
TOTAL SPECIMENS is highly correlated with TOTAL A and 5 other fieldsHigh correlation
TOTAL A is highly correlated with TOTAL SPECIMENS and 4 other fieldsHigh correlation
TOTAL B is highly correlated with TOTAL A and 2 other fieldsHigh correlation
PERCENT POSITIVE is highly correlated with TOTAL SPECIMENS and 4 other fieldsHigh correlation
PERCENT A is highly correlated with TOTAL SPECIMENS and 2 other fieldsHigh correlation
PERCENT B is highly correlated with TOTAL B and 1 other fieldsHigh correlation
ILITOTAL is highly correlated with TOTAL SPECIMENS and 3 other fieldsHigh correlation
NUM. OF PROVIDERS is highly correlated with TOTAL SPECIMENS and 2 other fieldsHigh correlation
TOTAL PATIENTS is highly correlated with TOTAL SPECIMENS and 2 other fieldsHigh correlation
TOTAL SPECIMENS is highly correlated with TOTAL A and 1 other fieldsHigh correlation
TOTAL A is highly correlated with TOTAL SPECIMENS and 3 other fieldsHigh correlation
TOTAL B is highly correlated with TOTAL A and 3 other fieldsHigh correlation
PERCENT POSITIVE is highly correlated with TOTAL A and 3 other fieldsHigh correlation
PERCENT A is highly correlated with TOTAL A and 2 other fieldsHigh correlation
PERCENT B is highly correlated with TOTAL B and 1 other fieldsHigh correlation
ILITOTAL is highly correlated with TOTAL SPECIMENS and 2 other fieldsHigh correlation
NUM. OF PROVIDERS is highly correlated with ILITOTAL and 1 other fieldsHigh correlation
TOTAL PATIENTS is highly correlated with ILITOTAL and 1 other fieldsHigh correlation
TOTAL SPECIMENS is highly correlated with TOTAL A and 9 other fieldsHigh correlation
TOTAL A is highly correlated with TOTAL SPECIMENS and 7 other fieldsHigh correlation
TOTAL B is highly correlated with TOTAL A and 4 other fieldsHigh correlation
PERCENT POSITIVE is highly correlated with TOTAL SPECIMENS and 7 other fieldsHigh correlation
PERCENT A is highly correlated with TOTAL SPECIMENS and 7 other fieldsHigh correlation
PERCENT B is highly correlated with TOTAL A and 6 other fieldsHigh correlation
start_year is highly correlated with TOTAL SPECIMENS and 3 other fieldsHigh correlation
start_month is highly correlated with TOTAL SPECIMENS and 5 other fieldsHigh correlation
start_day is highly correlated with end_dayHigh correlation
end_year is highly correlated with TOTAL SPECIMENS and 3 other fieldsHigh correlation
end_month is highly correlated with TOTAL SPECIMENS and 5 other fieldsHigh correlation
end_day is highly correlated with start_dayHigh correlation
ILITOTAL is highly correlated with TOTAL SPECIMENS and 7 other fieldsHigh correlation
NUM. OF PROVIDERS is highly correlated with TOTAL SPECIMENS and 4 other fieldsHigh correlation
TOTAL PATIENTS is highly correlated with TOTAL SPECIMENS and 4 other fieldsHigh correlation
start_date is uniformly distributed Uniform
end_date is uniformly distributed Uniform
start_day is uniformly distributed Uniform
end_day is uniformly distributed Uniform
start_date has unique values Unique
end_date has unique values Unique
TOTAL A has 113 (30.7%) zeros Zeros
TOTAL B has 181 (49.2%) zeros Zeros
PERCENT POSITIVE has 97 (26.4%) zeros Zeros
PERCENT A has 113 (30.7%) zeros Zeros
PERCENT B has 181 (49.2%) zeros Zeros

Reproduction

Analysis started2022-12-20 00:28:49.912091
Analysis finished2022-12-20 00:29:13.163883
Duration23.25 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

TOTAL SPECIMENS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct326
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean817.1222826
Minimum48
Maximum3303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:13.218497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile91.05
Q1295.5
median623
Q31146.5
95-th percentile2193.55
Maximum3303
Range3255
Interquartile range (IQR)851

Descriptive statistics

Standard deviation656.3662364
Coefficient of variation (CV)0.8032656193
Kurtosis0.5265617833
Mean817.1222826
Median Absolute Deviation (MAD)396.5
Skewness1.058313414
Sum300701
Variance430816.6362
MonotonicityNot monotonic
2022-12-19T19:29:13.298237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3193
 
0.8%
9593
 
0.8%
7272
 
0.5%
812
 
0.5%
1482
 
0.5%
4392
 
0.5%
8872
 
0.5%
10932
 
0.5%
9662
 
0.5%
7192
 
0.5%
Other values (316)346
94.0%
ValueCountFrequency (%)
481
0.3%
581
0.3%
591
0.3%
601
0.3%
611
0.3%
631
0.3%
651
0.3%
671
0.3%
691
0.3%
701
0.3%
ValueCountFrequency (%)
33031
0.3%
28731
0.3%
26321
0.3%
25731
0.3%
25551
0.3%
25121
0.3%
24671
0.3%
24591
0.3%
24471
0.3%
24441
0.3%

TOTAL A
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct117
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.07608696
Minimum0
Maximum644
Zeros113
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:13.375221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q340
95-th percentile309.9
Maximum644
Range644
Interquartile range (IQR)40

Descriptive statistics

Standard deviation114.4104884
Coefficient of variation (CV)1.970010282
Kurtosis6.307044606
Mean58.07608696
Median Absolute Deviation (MAD)2
Skewness2.479372058
Sum21372
Variance13089.75986
MonotonicityNot monotonic
2022-12-19T19:29:13.445323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0113
30.7%
147
 
12.8%
225
 
6.8%
49
 
2.4%
58
 
2.2%
37
 
1.9%
86
 
1.6%
66
 
1.6%
105
 
1.4%
354
 
1.1%
Other values (107)138
37.5%
ValueCountFrequency (%)
0113
30.7%
147
12.8%
225
 
6.8%
37
 
1.9%
49
 
2.4%
58
 
2.2%
66
 
1.6%
74
 
1.1%
86
 
1.6%
91
 
0.3%
ValueCountFrequency (%)
6441
0.3%
6411
0.3%
5671
0.3%
4961
0.3%
4512
0.5%
4341
0.3%
4271
0.3%
4132
0.5%
4121
0.3%
3891
0.3%

TOTAL B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct72
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.4048913
Minimum0
Maximum569
Zeros181
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:13.557440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile126.85
Maximum569
Range569
Interquartile range (IQR)7

Descriptive statistics

Standard deviation71.49277647
Coefficient of variation (CV)2.929444577
Kurtosis27.79454376
Mean24.4048913
Median Absolute Deviation (MAD)1
Skewness4.85945612
Sum8981
Variance5111.217088
MonotonicityNot monotonic
2022-12-19T19:29:13.630575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0181
49.2%
146
 
12.5%
218
 
4.9%
313
 
3.5%
48
 
2.2%
56
 
1.6%
123
 
0.8%
63
 
0.8%
963
 
0.8%
83
 
0.8%
Other values (62)84
22.8%
ValueCountFrequency (%)
0181
49.2%
146
 
12.5%
218
 
4.9%
313
 
3.5%
48
 
2.2%
56
 
1.6%
63
 
0.8%
73
 
0.8%
83
 
0.8%
102
 
0.5%
ValueCountFrequency (%)
5691
0.3%
5491
0.3%
5201
0.3%
4181
0.3%
3791
0.3%
3321
0.3%
3011
0.3%
2491
0.3%
2481
0.3%
2221
0.3%

PERCENT POSITIVE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct235
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.242907609
Minimum0
Maximum40.45
Zeros97
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:13.706445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.05
Q38.885
95-th percentile27.5195
Maximum40.45
Range40.45
Interquartile range (IQR)8.885

Descriptive statistics

Standard deviation9.606568369
Coefficient of variation (CV)1.538797139
Kurtosis1.651263697
Mean6.242907609
Median Absolute Deviation (MAD)1.05
Skewness1.632342003
Sum2297.39
Variance92.28615583
MonotonicityNot monotonic
2022-12-19T19:29:13.781815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
097
 
26.4%
0.684
 
1.1%
0.234
 
1.1%
0.094
 
1.1%
0.113
 
0.8%
0.353
 
0.8%
1.163
 
0.8%
0.992
 
0.5%
0.422
 
0.5%
0.332
 
0.5%
Other values (225)244
66.3%
ValueCountFrequency (%)
097
26.4%
0.061
 
0.3%
0.094
 
1.1%
0.12
 
0.5%
0.113
 
0.8%
0.121
 
0.3%
0.132
 
0.5%
0.142
 
0.5%
0.151
 
0.3%
0.161
 
0.3%
ValueCountFrequency (%)
40.451
0.3%
40.371
0.3%
38.931
0.3%
37.381
0.3%
36.611
0.3%
36.321
0.3%
33.941
0.3%
32.991
0.3%
32.741
0.3%
32.091
0.3%

PERCENT A
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct209
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.221820652
Minimum0
Maximum28.68
Zeros113
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:13.865456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.68
Q34.985
95-th percentile20.5865
Maximum28.68
Range28.68
Interquartile range (IQR)4.985

Descriptive statistics

Standard deviation6.908198123
Coefficient of variation (CV)1.636307814
Kurtosis2.301249295
Mean4.221820652
Median Absolute Deviation (MAD)0.68
Skewness1.828786143
Sum1553.63
Variance47.72320131
MonotonicityNot monotonic
2022-12-19T19:29:13.938454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0113
30.7%
0.685
 
1.4%
0.234
 
1.1%
0.24
 
1.1%
1.073
 
0.8%
0.663
 
0.8%
0.253
 
0.8%
0.423
 
0.8%
1.393
 
0.8%
0.143
 
0.8%
Other values (199)224
60.9%
ValueCountFrequency (%)
0113
30.7%
0.062
 
0.5%
0.091
 
0.3%
0.11
 
0.3%
0.112
 
0.5%
0.121
 
0.3%
0.131
 
0.3%
0.143
 
0.8%
0.152
 
0.5%
0.161
 
0.3%
ValueCountFrequency (%)
28.681
0.3%
28.21
0.3%
26.911
0.3%
26.281
0.3%
26.211
0.3%
26.191
0.3%
26.121
0.3%
25.311
0.3%
24.911
0.3%
24.531
0.3%

PERCENT B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct143
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.021168478
Minimum0
Maximum25.97
Zeros181
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:14.015066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.05
Q31.1575
95-th percentile11.7
Maximum25.97
Range25.97
Interquartile range (IQR)1.1575

Descriptive statistics

Standard deviation4.268316643
Coefficient of variation (CV)2.111806457
Kurtosis7.733676499
Mean2.021168478
Median Absolute Deviation (MAD)0.05
Skewness2.709149112
Sum743.79
Variance18.21852697
MonotonicityNot monotonic
2022-12-19T19:29:14.093315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0181
49.2%
0.115
 
1.4%
0.095
 
1.4%
0.064
 
1.1%
0.433
 
0.8%
0.283
 
0.8%
0.083
 
0.8%
0.193
 
0.8%
0.463
 
0.8%
0.133
 
0.8%
Other values (133)155
42.1%
ValueCountFrequency (%)
0181
49.2%
0.031
 
0.3%
0.041
 
0.3%
0.052
 
0.5%
0.064
 
1.1%
0.073
 
0.8%
0.083
 
0.8%
0.095
 
1.4%
0.11
 
0.3%
0.115
 
1.4%
ValueCountFrequency (%)
25.971
0.3%
23.061
0.3%
21.271
0.3%
20.71
0.3%
18.391
0.3%
17.891
0.3%
17.41
0.3%
15.821
0.3%
15.591
0.3%
14.271
0.3%

start_date
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct368
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2015-10-04
 
1
2020-05-30
 
1
2020-08-01
 
1
2020-07-25
 
1
2020-07-18
 
1
Other values (363)
363 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique368 ?
Unique (%)100.0%

Sample

1st row2015-10-04
2nd row2015-10-11
3rd row2015-10-18
4th row2015-10-25
5th row2015-11-01

Common Values

ValueCountFrequency (%)
2015-10-041
 
0.3%
2020-05-301
 
0.3%
2020-08-011
 
0.3%
2020-07-251
 
0.3%
2020-07-181
 
0.3%
2020-07-111
 
0.3%
2020-07-041
 
0.3%
2020-06-271
 
0.3%
2020-06-201
 
0.3%
2020-06-131
 
0.3%
Other values (358)358
97.3%

Length

2022-12-19T19:29:14.156100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-10-041
 
0.3%
2015-12-201
 
0.3%
2015-10-181
 
0.3%
2015-10-251
 
0.3%
2015-11-011
 
0.3%
2015-11-081
 
0.3%
2015-11-151
 
0.3%
2015-11-221
 
0.3%
2015-11-291
 
0.3%
2015-12-061
 
0.3%
Other values (358)358
97.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

end_date
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct368
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2015-10-10
 
1
2020-06-05
 
1
2020-08-07
 
1
2020-07-31
 
1
2020-07-24
 
1
Other values (363)
363 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique368 ?
Unique (%)100.0%

Sample

1st row2015-10-10
2nd row2015-10-17
3rd row2015-10-24
4th row2015-10-31
5th row2015-11-07

Common Values

ValueCountFrequency (%)
2015-10-101
 
0.3%
2020-06-051
 
0.3%
2020-08-071
 
0.3%
2020-07-311
 
0.3%
2020-07-241
 
0.3%
2020-07-171
 
0.3%
2020-07-101
 
0.3%
2020-07-031
 
0.3%
2020-06-261
 
0.3%
2020-06-191
 
0.3%
Other values (358)358
97.3%

Length

2022-12-19T19:29:14.235429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-10-101
 
0.3%
2015-12-261
 
0.3%
2015-10-241
 
0.3%
2015-10-311
 
0.3%
2015-11-071
 
0.3%
2015-11-141
 
0.3%
2015-11-211
 
0.3%
2015-11-281
 
0.3%
2015-12-051
 
0.3%
2015-12-121
 
0.3%
Other values (358)358
97.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

start_year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.777174
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:14.286752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.064180212
Coefficient of variation (CV)0.001022490366
Kurtosis-1.159109412
Mean2018.777174
Median Absolute Deviation (MAD)2
Skewness-0.01638807909
Sum742910
Variance4.260839948
MonotonicityIncreasing
2022-12-19T19:29:14.338553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
201753
14.4%
202153
14.4%
201652
14.1%
201852
14.1%
202052
14.1%
201951
13.9%
202242
11.4%
201513
 
3.5%
ValueCountFrequency (%)
201513
 
3.5%
201652
14.1%
201753
14.4%
201852
14.1%
201951
13.9%
202052
14.1%
202153
14.4%
202242
11.4%
ValueCountFrequency (%)
202242
11.4%
202153
14.4%
202052
14.1%
201951
13.9%
201852
14.1%
201753
14.4%
201652
14.1%
201513
 
3.5%

start_month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.508152174
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:14.398249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.460156847
Coefficient of variation (CV)0.5316650186
Kurtosis-1.214061342
Mean6.508152174
Median Absolute Deviation (MAD)3
Skewness-0.0235240064
Sum2395
Variance11.9726854
MonotonicityNot monotonic
2022-12-19T19:29:14.455401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1035
9.5%
133
9.0%
532
8.7%
732
8.7%
1230
8.2%
430
8.2%
830
8.2%
930
8.2%
1129
7.9%
229
7.9%
Other values (2)58
15.8%
ValueCountFrequency (%)
133
9.0%
229
7.9%
329
7.9%
430
8.2%
532
8.7%
629
7.9%
732
8.7%
830
8.2%
930
8.2%
1035
9.5%
ValueCountFrequency (%)
1230
8.2%
1129
7.9%
1035
9.5%
930
8.2%
830
8.2%
732
8.7%
629
7.9%
532
8.7%
430
8.2%
329
7.9%

start_day
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM

Distinct31
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.63586957
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:14.517333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.800422104
Coefficient of variation (CV)0.562835477
Kurtosis-1.192104955
Mean15.63586957
Median Absolute Deviation (MAD)8
Skewness0.01858304804
Sum5754
Variance77.44742921
MonotonicityNot monotonic
2022-12-19T19:29:14.600846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
414
 
3.8%
314
 
3.8%
2414
 
3.8%
1114
 
3.8%
1014
 
3.8%
1714
 
3.8%
1814
 
3.8%
2514
 
3.8%
713
 
3.5%
2813
 
3.5%
Other values (21)230
62.5%
ValueCountFrequency (%)
112
3.3%
211
3.0%
314
3.8%
414
3.8%
511
3.0%
611
3.0%
713
3.5%
812
3.3%
910
2.7%
1014
3.8%
ValueCountFrequency (%)
318
2.2%
309
2.4%
2911
3.0%
2813
3.5%
2711
3.0%
2611
3.0%
2514
3.8%
2414
3.8%
239
2.4%
2212
3.3%

end_year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.790761
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:14.694674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.063625937
Coefficient of variation (CV)0.001022208927
Kurtosis-1.164372418
Mean2018.790761
Median Absolute Deviation (MAD)2
Skewness-0.01452027121
Sum742915
Variance4.258552008
MonotonicityIncreasing
2022-12-19T19:29:14.747792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
201653
14.4%
202153
14.4%
201752
14.1%
201852
14.1%
201952
14.1%
202051
13.9%
202243
11.7%
201512
 
3.3%
ValueCountFrequency (%)
201512
 
3.3%
201653
14.4%
201752
14.1%
201852
14.1%
201952
14.1%
202051
13.9%
202153
14.4%
202243
11.7%
ValueCountFrequency (%)
202243
11.7%
202153
14.4%
202051
13.9%
201952
14.1%
201852
14.1%
201752
14.1%
201653
14.4%
201512
 
3.3%

end_month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.538043478
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:14.816522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.456805234
Coefficient of variation (CV)0.5287216651
Kurtosis-1.211414686
Mean6.538043478
Median Absolute Deviation (MAD)3
Skewness-0.02520578192
Sum2406
Variance11.94950243
MonotonicityNot monotonic
2022-12-19T19:29:14.903579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1035
9.5%
733
9.0%
132
8.7%
1231
8.4%
431
8.4%
330
8.2%
530
8.2%
630
8.2%
930
8.2%
1129
7.9%
Other values (2)57
15.5%
ValueCountFrequency (%)
132
8.7%
228
7.6%
330
8.2%
431
8.4%
530
8.2%
630
8.2%
733
9.0%
829
7.9%
930
8.2%
1035
9.5%
ValueCountFrequency (%)
1231
8.4%
1129
7.9%
1035
9.5%
930
8.2%
829
7.9%
733
9.0%
630
8.2%
530
8.2%
431
8.4%
330
8.2%

end_day
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM

Distinct31
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.76902174
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:14.976093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.778436369
Coefficient of variation (CV)0.55668871
Kurtosis-1.184479305
Mean15.76902174
Median Absolute Deviation (MAD)7.5
Skewness0.008256533131
Sum5803
Variance77.06094509
MonotonicityNot monotonic
2022-12-19T19:29:15.094804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1014
 
3.8%
914
 
3.8%
1714
 
3.8%
1614
 
3.8%
2314
 
3.8%
2414
 
3.8%
1313
 
3.5%
2713
 
3.5%
2013
 
3.5%
613
 
3.5%
Other values (21)232
63.0%
ValueCountFrequency (%)
111
3.0%
213
3.5%
312
3.3%
411
3.0%
511
3.0%
613
3.5%
712
3.3%
811
3.0%
914
3.8%
1014
3.8%
ValueCountFrequency (%)
317
1.9%
3013
3.5%
299
2.4%
2812
3.3%
2713
3.5%
2611
3.0%
2511
3.0%
2414
3.8%
2314
3.8%
2210
2.7%

ILITOTAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct323
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean901.3641304
Minimum27
Maximum6300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:15.443766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile90.05
Q1287.75
median551
Q31118
95-th percentile3242.1
Maximum6300
Range6273
Interquartile range (IQR)830.25

Descriptive statistics

Standard deviation1017.475734
Coefficient of variation (CV)1.128817644
Kurtosis5.901381464
Mean901.3641304
Median Absolute Deviation (MAD)325.5
Skewness2.337895781
Sum331702
Variance1035256.87
MonotonicityNot monotonic
2022-12-19T19:29:15.515790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1774
 
1.1%
3824
 
1.1%
2103
 
0.8%
3123
 
0.8%
5993
 
0.8%
2622
 
0.5%
922
 
0.5%
1512
 
0.5%
1982
 
0.5%
2302
 
0.5%
Other values (313)341
92.7%
ValueCountFrequency (%)
271
0.3%
551
0.3%
601
0.3%
641
0.3%
652
0.5%
691
0.3%
711
0.3%
721
0.3%
741
0.3%
771
0.3%
ValueCountFrequency (%)
63001
0.3%
51221
0.3%
49861
0.3%
48121
0.3%
46361
0.3%
44601
0.3%
43841
0.3%
41581
0.3%
41381
0.3%
41301
0.3%

NUM. OF PROVIDERS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct72
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.29891304
Minimum12
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:15.591440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile24
Q145
median60
Q392
95-th percentile184
Maximum186
Range174
Interquartile range (IQR)47

Descriptive statistics

Standard deviation44.77339614
Coefficient of variation (CV)0.5868156485
Kurtosis0.8316480511
Mean76.29891304
Median Absolute Deviation (MAD)21.5
Skewness1.275595198
Sum28078
Variance2004.657002
MonotonicityNot monotonic
2022-12-19T19:29:15.664267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5825
 
6.8%
9223
 
6.2%
4517
 
4.6%
4416
 
4.3%
18616
 
4.3%
9313
 
3.5%
4212
 
3.3%
5712
 
3.3%
18411
 
3.0%
6011
 
3.0%
Other values (62)212
57.6%
ValueCountFrequency (%)
121
 
0.3%
201
 
0.3%
225
1.4%
234
1.1%
249
2.4%
257
1.9%
263
 
0.8%
272
 
0.5%
285
1.4%
321
 
0.3%
ValueCountFrequency (%)
18616
4.3%
18411
3.0%
1823
 
0.8%
1802
 
0.5%
1683
 
0.8%
1661
 
0.3%
1621
 
0.3%
1561
 
0.3%
1542
 
0.5%
1521
 
0.3%

TOTAL PATIENTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct366
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58170.49457
Minimum6273
Maximum164848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-12-19T19:29:15.742035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6273
5-th percentile18749.5
Q128108
median51021
Q368488.5
95-th percentile144038.8
Maximum164848
Range158575
Interquartile range (IQR)40380.5

Descriptive statistics

Standard deviation37139.78256
Coefficient of variation (CV)0.6384642736
Kurtosis0.731831206
Mean58170.49457
Median Absolute Deviation (MAD)20337
Skewness1.244901681
Sum21406742
Variance1379363449
MonotonicityNot monotonic
2022-12-19T19:29:15.817649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234052
 
0.5%
306242
 
0.5%
317161
 
0.3%
412211
 
0.3%
410441
 
0.3%
396651
 
0.3%
397121
 
0.3%
387661
 
0.3%
346221
 
0.3%
350781
 
0.3%
Other values (356)356
96.7%
ValueCountFrequency (%)
62731
0.3%
122781
0.3%
149631
0.3%
152341
0.3%
154561
0.3%
155271
0.3%
156101
0.3%
164831
0.3%
166981
0.3%
169121
0.3%
ValueCountFrequency (%)
1648481
0.3%
1600361
0.3%
1521321
0.3%
1505921
0.3%
1505901
0.3%
1496181
0.3%
1495261
0.3%
1479481
0.3%
1468401
0.3%
1465801
0.3%

Interactions

2022-12-19T19:29:11.816655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:55.291307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:56.299744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:57.838011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:58.923498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:00.059116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:01.432868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:02.409635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:03.511737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:04.524285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:06.176066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:07.192539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:08.206735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:09.557226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:10.820243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:11.886265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:55.354188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:56.360913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:57.929896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:58.990454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:00.122086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:01.496121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:02.488533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:03.575517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:04.609103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:06.241386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:07.255300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:08.292810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:09.618159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:10.892792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:11.950507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:55.414503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:56.420687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:57.994291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:59.052532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:00.585057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:01.561088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:02.571842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:03.637613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:04.692300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:06.310042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:07.325919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:08.374588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:09.677870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:10.951575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:12.012135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:55.482960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:56.478523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:58.092983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:59.111708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:00.641235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:01.614806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:02.637822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:03.698458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:04.775937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:06.373241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:07.388908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:08.476999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:09.736110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:11.008423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:12.078491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:55.547873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:56.543435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:58.173298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:28:59.176010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:00.702335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:01.677022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:02.710599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:03.765357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:04.866327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:06.450866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:07.456524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T19:29:09.801270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:11.071720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-19T19:29:12.147826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T19:28:56.605058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T19:28:59.248084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T19:28:59.757101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T19:29:07.918144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T19:28:57.504595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-19T19:29:11.752376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-19T19:29:15.886352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-19T19:29:15.987592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-19T19:29:16.107213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-19T19:29:16.209543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-19T19:29:12.935877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-19T19:29:13.110548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TOTAL SPECIMENSTOTAL ATOTAL BPERCENT POSITIVEPERCENT APERCENT Bstart_dateend_datestart_yearstart_monthstart_dayend_yearend_monthend_dayILITOTALNUM. OF PROVIDERSTOTAL PATIENTS
0259121.160.390.772015-10-042015-10-1020151004201510101854423405
1256110.780.390.392015-10-112015-10-1720151011201510171344320815
2244010.410.000.412015-10-182015-10-2420151018201510241774424135
3231010.430.000.432015-10-252015-10-3120151025201510311424124071
4273000.000.000.002015-11-012015-11-0720151101201511071644424554
5218110.920.460.462015-11-082015-11-1420151108201511141654524543
6263000.000.000.002015-11-152015-11-2120151115201511211624425102
7287200.700.700.002015-11-222015-11-2820151122201511281474216912
8286110.700.350.352015-11-292015-12-0520151129201512052004625220
9303030.990.000.992015-12-062015-12-1220151206201512122024624901

Last rows

TOTAL SPECIMENSTOTAL ATOTAL BPERCENT POSITIVEPERCENT APERCENT Bstart_dateend_datestart_yearstart_monthstart_dayend_yearend_monthend_dayILITOTALNUM. OF PROVIDERSTOTAL PATIENTS
3581621100.060.060.002022-08-142022-08-2020220814202208201088146133270
3591504300.200.200.002022-08-212022-08-2720220821202208271118150136460
3601529500.330.330.002022-08-282022-09-0320220828202209031042150135500
3611567700.450.450.002022-09-042022-09-1020220904202209101222156133664
3621787300.170.170.002022-09-112022-09-1720220911202209171656154146580
36318911300.690.690.002022-09-182022-09-2420220918202209242032168164848
36419921000.500.500.002022-09-252022-10-0120220925202210012226168160036
36523621500.640.640.002022-10-022022-10-08202210022022100812699280301
36623881620.750.670.082022-10-092022-10-15202210092022101514119481762
36723322511.111.070.042022-10-162022-10-22202210162022102213489382281